Univ. Grenoble Alpes, Grenoble, France, CEA, LETI, MINATEC Campus, Grenoble, France, Safran Identity and Security, Issy-les-Moulineaux, France, Sorbonne Universités, UPMC Univ Paris 06, POLSYS, UMR 7606, LIP6, Paris, France

In the context of the security evaluation of cryptographic implementations, profiling attacks (aka Template Attacks) play a fundamental role. Nowadays the most popular Template Attack strategy consists in approximating the information leakages by Gaussian distributions. Nevertheless this approach suffers from the difficulty to deal with both the traces misalignment and the high dimensionality of the data. This forces the attacker to perform critical preprocessing phases, such as the selection of the points of interest and the realignment of measurements. Some software and hardware countermeasures have been conceived exactly to create such a misalignment. In this paper we propose an end-to-end profiling attack strategy based on the Convolutional Neural Networks: this strategy greatly facilitates the attack roadmap, since it does not require a previous trace realignment nor a precise selection of points of interest. To significantly increase the performances of the CNN, we moreover propose to equip it with the data augmentation technique that is classical in other applications of Machine Learning. As a validation, we present several experiments against traces misaligned by different kinds of countermeasures, including the augmentation of the clock jitter effect in a secure hardware implementation over a modern chip. The excellent results achieved in these experiments prove that Convolutional Neural Networks approach combined with data augmentation gives a very efficient alternative to the state-of-the-art profiling attacks. © International Association for Cryptologic Research 2017.

Convolutional neural networks, Data augmentation, Jitter, Machine learning, Side-channel attacks, Trace misalignment, Unstable clock

Alignment, Artificial intelligence, Clocks, Convolution, Cryptography, Embedded systems, Hardware, Jitter, Learning systems, Neural networks, Clock jitter effects, Convolutional neural network, Cryptographic implementation, Data augmentation, Hardware implementations, Profiling attack strategies, Software and hardwares, Trace misalignment, Side channel attack